Title :
Improving active learning methods using spatial information
Author :
Pasolli, Edoardo ; Melgani, Farid ; Tuia, Devis ; Pacifici, Fabio ; Emery, William J.
Author_Institution :
Dept. of Inf. Eng. & Comput. Sci., Univ. of Trento, Trento, Italy
Abstract :
Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning.
Keywords :
geophysical image processing; image classification; image resolution; learning (artificial intelligence); remote sensing; active learning; remote sensing image classification; spatial information; spectral criterion; terrain campaign planning; very high resolution image; Accuracy; Learning systems; Machine learning; Remote sensing; Spatial resolution; Support vector machines; Training; Active learning; spatial information; support vector machines (SVMs); very-high-resolution (VHR) images;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4577-1003-2
DOI :
10.1109/IGARSS.2011.6050089
